Practical AI in Healthcare

In this episode of Practical AI in Healthcare, we sit down with physician–informaticist Josh Geleris, MD, co-founder and Chief Product Officer of SmarterDx, to unpack one of healthcare’s most overlooked AI opportunities: revenue cycle intelligence. Drawing on his clinical training, deep technical background, and firsthand experience inside large health systems, Josh explains how AI can bridge the gap between clinical reality and billing documentation. The conversation explores how machine learning and large language models translate thousands of data points from an inpatient stay into accurate, compliant coding, helping health systems reduce revenue leakage while staying firmly within regulatory guardrails. From SQL queries to post-trained LLMs, Josh walks us through the evolution of SmarterDx’s AI stack and why human-in-the-loop design remains essential. This is a grounded, practical look at AI delivering real value where healthcare operations and clinical truth collide.

In this episode of Practical AI in Healthcare, we sit down with Dr. Alvin Liu, retinal surgeon and Professor of Artificial Intelligence and Ophthalmology at Johns Hopkins University, to explore one of the earliest and most successful real-world deployments of medical AI.Dr. Liu walks us through the evolution of autonomous AI for diabetic retinopathy screening, from FDA approval to large-scale clinical implementation across health systems. We unpack what it really takes to move AI from validation to impact, including workflow integration, sensitivity and specificity tradeoffs, reimbursement challenges, and post-market monitoring. The conversation also looks ahead to emerging AI applications using retinal imaging to predict cardiovascular disease, dementia, and kidney disease at the population level.This episode is a masterclass in how AI can meaningfully improve access, equity, and outcomes in healthcare when deployed thoughtfully and responsibly.

In this episode of Practical AI in Healthcare, we sit down with Dr. Tiffany Leung, Scientific Editorial Director at JMIR Publications, to explore how artificial intelligence is reshaping scientific publishing from the inside out. As open access journals face unprecedented volumes of submissions, AI is simultaneously enabling faster discovery and creating new challenges around research integrity, peer review, and trust in the scientific record.Tiffany shares how journals are adapting to generative AI tools, from policy development and disclosure norms to editorial decision support systems that help identify potential risks without stifling innovation. The conversation moves beyond hype to examine how AI can act as a co scientist, streamline editorial workflows, and potentially redefine peer review itself. This episode offers a rare look at how AI is influencing not just what gets published, but how knowledge is validated and shared globally.

Kenny White, an insurance veteran who helps health systems spot and finance risk. Kathy Roe, a health lawyer advising on regulation, privacy, and care delivery. They unpack liability differences across AI types. They explore HIPAA and de-identification limits. They cover intellectual property, contractual risk, and how insurers and courts are adapting to AI in healthcare.

In our second Reflections episode, Steve Labkoff and Leon Rozenblit synthesize the most meaningful insights from guests across Episodes 9–15. Drawing on conversations with Orr Inbar, Martin Leach, Ing Ho, Yuri Quintana, and patient advocate E-Patient Dave, this episode highlights the themes shaping practical AI adoption in today’s healthcare landscape.Key topics include the inflection point AI has created across clinical care, research, and patient engagement; the need for stronger data stewardship to support trustworthy automation; and the emerging promise of AI-driven clinical trial optimization and simulation. We also explore how patients are engaging with AI tools independently, raising new questions about literacy, safety, and empowerment.Additional themes include the cultural alignment required for tools like ambient listening and chart summarization to succeed and what it will take for AI to avoid the missteps of past health-IT transitions.The episode closes with a preview of upcoming guests discussing AI in law, payer innovation, psychiatric diagnostics, and the future of scientific publishing.

Adam Rodman, physician, informatician, and historian of medicine, explores how large language models reshape clinical reasoning. He traces the arc from early diagnostic systems to GPT breakthroughs. He discusses workflow redesigns for clinician–AI collaboration, regulatory and liability gaps, patient-first AI agents, and early signals from urgent-care AI startups.

Practical AI in Healthcare just released one of our most eye-opening conversations yet, featuring Orr Inbar, CEO & Co-Founder of QuantHealth.QuantHealth is pushing the boundaries of what’s possible in clinical development with AI-driven clinical trial simulation. Orr walked us through how modern deep learning, massive biological knowledge graphs, and patient-level real-world data can now simulate clinical trials with 80–90% accuracy — across dozens of indications, modalities, and trial phases.In our conversation, we cover:• How QuantHealth models patient-drug interactions at massive scale• Why trial design is still the most critical (and fixable) failure point in drug development• How simulation is becoming the new starting point for protocols• What AI-first trial design could mean for speed, cost, and reducing avoidable trial failures• Where the field is headed in the next 5–10 years — including the provocative question of how far simulation can replace human trialsOrr also discusses the acceleration of biological data, the maturity of real-world data, and transformer-based AI — the perfect storm that made this moment possible.This is one of the best deep dives yet into the practical future of drug development.

Dr. Yin Ho, a Health IT strategist and author, brings sharp perspective on healthcare technology and responsible AI. She examines risks of large LLMs, the case for smaller domain-specific models, and why data quality and provenance matter. Topics include decision support versus control, ambient scribing pitfalls, and strategies to avoid repeating past digital missteps.

Dr. S. Yin Ho, physician, entrepreneur, and author of Rushing Headlong, reflects on 25 years in health IT. She traces early electronic records, explains how design choices and billing incentives shaped today’s fragmented systems, and outlines the divide between clinical care records and research-ready data. Conversation sets up part two on generative AI.

Dr. Yuri Quintana, Director of the Division of Clinical Informatics at Beth Israel Deaconess Medical Center, joins us this week to discuss the DCI Network’s mission: turning AI in healthcare from theory into practice.The conversation traces DCI’s evolution as a multi-stakeholder “action tank” focused on collaboration, transparency, and patient safety. Quintana recounts the success of the Signal Through the Noise conference and the insights that emerged—chief among them that “mundane AI” (like triage, scheduling, and documentation tools) is quietly creating real value.The episode also explores topics like AI literacy, governance, transparency (“AI nutrition (model) labels”), and post-market surveillance for clinical AI—ending with a call to action: keeping healthcare innovation grounded in what truly matters to patients.